CoorsTek, Inc.

Machine Learning Intern · June 2024 – March 2026 · Golden, CO · Hybrid

Role Overview

Machine Learning Intern

CoorsTek is an advanced technical ceramics manufacturer — one of the largest in the world — serving industries from semiconductor fabrication to aerospace. I completed three consecutive internships within the R&D engineering group, building production ML systems that shipped to internal users.

Across 14 months I owned the full lifecycle of four distinct projects: two computer vision tools, a graph-based material analysis system, and an internal agentic RAG chatbot. Each project addressed a real operational or research need and was deployed within the company.

Internship 1 · Jun 2024 – Dec 2024

Fabricated ML and data-driven solutions to accelerate R&D workflows
Engineered data collection and storage methods to preserve data integrity
Employed data analysis techniques to give engineers insight into their data

Internship 2 · May 2025 – Aug 2025

Developed Databricks-deployed apps connecting engineers to their data
Utilized Databricks to develop generative AI solutions for a technical environment
Continued support and development of existing deep learning tools

Internship 3 · Jan 2026 – Mar 2026

Pioneered an internal, Databricks-deployed agentic chatbot for the R&D group — 10× reduction in data retrieval time for engineers
Provided production-quality AI-powered defect detection to the manufacturing group — improved accuracy from ~80% to ~98%
Developed standard procedures for DataOps and MLOps for production-deployed products within the company
Instilled Agile development practices within the team; consulted with customers to understand and meet development needs

Python PyTorch Databricks SQL Computer Vision RAG MLOps LLMOps Graph Theory

Projects at CoorsTek

Work I Built Here

Four projects spanning computer vision, graph-based data modeling, and generative AI — all deployed to internal users.

Work

Graph-Based Material History

Represented ceramic manufacturing histories as directed graphs to capture formulation transformations across production steps. Built core graph logic (Internship 1) and an interactive research app on top of it (Internship 2).

Python Graph Theory SQL Streamlit

~30 minSaved per query

Work

Semantic Segmentation for Ceramic Parts

Deep learning pipeline to geometrically analyze ceramic parts at multiple production stages (spec → green → fired), enabling researchers to compare internal features across the manufacturing process.

Python PyTorch Computer Vision

95%Faster data collection

Work

Internal RAG Chatbot

Agentic chatbot connecting R&D engineers to their data via Databricks Agent Bricks. Zero to first deployment in 2 months. Established LLMOps and agent evaluation SOPs company-wide. Achieved 10× reduction in data retrieval time.

Python Databricks RAG LLMOps

95+%Agent reliability

10×Faster retrieval

Work

Discoloration Defect Detection

Production CV system for automated quality control in manufacturing. Identified and fixed data quality issues, improved the model development procedure, and delivered a standard MLOps workflow for future production deployments.

Python PyTorch Computer Vision MLOps

~98%Accuracy

~80%→~98%Improvement

← All Work Experience